15,005 research outputs found
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
Performance analysis of a parallel, multi-node pipeline for DNA sequencing
Post-sequencing DNA analysis typically consists of read mapping followed by variant calling and is very time-consuming, even on a multi-core machine. Recently, we proposed Halvade, a parallel, multi-node implementation of a DNA sequencing pipeline according to the GATK Best Practices recommendations. The MapReduce programming model is used to distribute the workload among different workers. In this paper, we study the impact of different hardware configurations on the performance of Halvade. Benchmarks indicate that especially the lack of good multithreading capabilities in the existing tools (BWA, SAMtools, Picard, GATK) cause suboptimal scaling behavior. We demonstrate that it is possible to circumvent this bottleneck by using multiprocessing on high-memory machines rather than using multithreading. Using a 15-node cluster with 360 CPU cores in total, this results in a runtime of 1 h 31 min. Compared to a single-threaded runtime of similar to 12 days, this corresponds to an overall parallel efficiency of 53%
Benchmarking database systems for Genomic Selection implementation
Motivation: With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results: We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix
Design and evaluation of a genomics variant analysis pipeline using GATK Spark tools
Scalable and efficient processing of genome sequence data, i.e. for variant
discovery, is key to the mainstream adoption of High Throughput technology for
disease prevention and for clinical use. Achieving scalability, however,
requires a significant effort to enable the parallel execution of the analysis
tools that make up the pipelines. This is facilitated by the new Spark versions
of the well-known GATK toolkit, which offer a black-box approach by
transparently exploiting the underlying Map Reduce architecture. In this paper
we report on our experience implementing a standard variant discovery pipeline
using GATK 4.0 with Docker-based deployment over a cluster. We provide a
preliminary performance analysis, comparing the processing times and cost to
those of the new Microsoft Genomics Services
khmer: Working with Big Data in Bioinformatics
We introduce design and optimization considerations for the 'khmer' package.Comment: Invited chapter for forthcoming book on Performance of Open Source
Application
- …